Human-Object Interaction (HOI) detection is a task to predict a set of \(\langle \)human, object, interaction\(\rangle \)  triplets in an image [1]}.
Previous methods have indirectly addressed this task by detecting human and object instances and individually inferring interaction labels for every pair of the detected instances with either neural networks (, two-stage HOI detectors [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]}, [20]}) or triplet matching (, one-stage HOI detectors [21]}, [22]}, [23]}).
The additional complexity caused by this indirect inference structure and post-processing (, NMS) stage behaved as a major bottleneck in inference time in HOI detection.
To deal with this bottleneck, transformer-based HOI detectors [24]}, [25]}, [26]}, [27]} have been proposed to achieve end-to-end HOI detection without the need for the post-processing stage mentioned above.
These works have shown competitive performance in both accuracy and inference time with direct set-level prediction and transformer attentions that can exploit the contextual information between humans, objects, and their interactions.
